The Re-Label Method For Data-Centric Machine Learning
cs.LG, cs.CL
Submitted: 2023-02-09
Updated: 2026-10-15
License: http://creativecommons.org/licenses/by/4.0/
The gist: In industry deep learning application, our manually labeled data has a certain number of noisy data.
Abstract
In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The dev dataset evaluation results and human evaluation results verify our idea.
Sources
- Learning From How Humans Correct
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
- Training language models to follow instructions with human feedback
- Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection
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